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338 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 6, NO. 2, JUNE 2011
Fingerprint Matching Incorporating Ridge
Features With Minutiae
Heeseung Choi, Kyoungtaek Choi, and Jaihie Kim
Abstract—This paper introduces a novel fingerprint matching
algorithm using both ridge features and the conventional minu-
tiae feature to increase the recognition performance against non-
linear deformation in fingerprints. The proposed ridge features are
composed of four elements: ridge count, ridge length, ridge cur-
vature direction, and ridge type. These ridge features have some
advantages in that they can represent the topology information in
entire ridge patterns existing between two minutiae and are not
changed by nonlinear deformation of the finger. For extracting
ridge features, we also define the ridge-based coordinate system
in a skeletonized image. With the proposed ridge features and con-
ventional minutiae features (minutiae type, orientation, and posi-
tion), we propose a novel matching scheme using a breadth-first
search to detect the matched minutiae pairs incrementally. Fol-
lowing that, the maximum score is computed and used as the final
matching score of two fingerprints. Experiments were conducted
for the FVC2002 and FVC2004 databases to compare the proposed
method with the conventional minutiae-based method. The pro-
posed method achieved higher matching scores. Thus, we conclude
that the proposed ridge feature gives additional information for
fingerprint matching with little increment in template size and can
be used in conjunction with existing minutiae features to increase
the accuracy and robustness of fingerprint recognition systems.
Index Terms—Breadth first search, ridge count, ridge features,
ridge-based coordinate system.
I. INTRODUCTION
FINGERPRINT recognition has been widely adopted
foruseridentification due to its reliable performance,
usability, and low cost compared with other biometrics such as
signature, iris, face, and gait recognition [1]. It is used in a wide
range of forensic and commercial applications, e.g., criminal
investigation, e-commerce, and electronic personal ID cards.
Although significant improvement in fingerprint recognition
has been achieved, many challenging tasks still remain. Among
them, nonlinear distortions, presented in touch-based finger-
print sensing, make fingerprint matching more difficult. As
showninFig.1,eventhoughthesetwofingerprint images are
from the same individual, the relative positions of the minutiae
Manuscript received July 15, 2010; revised December 10, 2010; accepted
December 18, 2010. Date of publication January 06, 2011; date of current ver-
sion May 18, 2011. This work was supported by the Korea Science and En-
gineering Foundation (KOSEF) through the Biometrics Engineering Research
Center at Yonsei University R112002105070010(2010). The associate editor
coordinating the review of this manuscript and approving it for publication was
Dr.ArunRoss.
H.ChoiandJ.KimarewiththeBiometric Engineering Research Center,
Yonsei University, Seoul 120-749, Korea (e-mail: mcnas@yonsei.ac.kr;
jhkim@yonsei.ac.kr).
K. Choi is with the LIG Nex1 Company, Yong In, Geyonggi, 148-1, Korea
(e-mail: kyoungtaekchoi@lignex1.com).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIFS.2010.2103940
Fig. 1. Example of skin distortions.
are very different due to skin distortions. This distortion is an
inevitable problem since it is usually associated with several
parameters [2], [3], including skin elasticity, nonuniform pres-
sure applied by the subject, different finger placement with the
sensor, etc.
To deal with the distortions in fingerprint images and im-
prove the matching performance, various methods have been
proposed by many researchers. These can be roughly classi-
fied into several groups: modeling the distortion of fingerprints
[2], [4], [5]; detecting the distortions using special hardware or
video sequences [6], [7]; allowing some amount of distortion
in the minutiae matching stages [8], [9]; and using local simi-
larity measures [10], [11]. Cappelli et al. [5] proposed a plastic
distortion model of a fingerprint to calculate the nonlinear defor-
mation of fingerprints. They first defined three distinct regions
in fingerprint images and described the distortion mechanism
using some distortion parameters. This method has successfully
been applied to the generation of synthetic fingerprints of the
same finger [12]. However, it is hard to estimate the parame-
ters accurately due to insufficient information. Bazen et al. [4]
and Ross et al. [2] used the thin plate spline (TPS) model to
compensate for deformations, but this sort of alignment process
typically requires too much computational power to be used in
practical fingerprint recognition systems. Ratha et al. [6] pro-
posed a method to directly measure the forces and torques on
the scanner using special hardware and Dorai et al. [7] estimated
the distortions by observing fingerprint video sequences. These
two methods prevent the capture of severely distorted images,
but they cannot detect the distortion in data sets that were col-
lected in the past and the need of additional hardware restricts
their use in practical situations. Luo et al. [8] used changeable
tolerance boxes in the minutiae matching process. The size of
the tolerance boxes is incrementally increased, moving from
the center towards the borders of the fingerprint area, to deal
with the effect of distortion. Lee et al. [9] applied distance nor-
malization and local alignment during minutiae matching. In
local minutiae matching, these approaches can be considered
1556-6013/$26.00 © 2011 IEEE
CHOI et al.: FINGERPRINT MATCHING INCORPORATING RIDGE FEATURES WITH MINUTIAE 339
as an effective tool. However, as the size of the tolerance boxes
had to be increased, the probability of falsely matching finger-
prints from different fingers also increases. Some methods used
local similarity measures to improve the robustness of the dis-
tortions since fingerprint images are less affected by distortions
in the local area. Jiang et al. [10] proposed a method using the
similarity measure defined between local structural features to
align fingerprint images. Kovacs–Vajna [11] proposed a method
using triangular matching by checking the correspondence of
gray-scale profiles between every pair of minutiae from the tem-
plate and the corresponding positions in the input images. And
the dynamic time warping method was used to tolerate small lo-
cation errors of features. However, for widespread use of these
approaches, a consolidation step may be implemented to check
whether the local similarity holds at the global level [1].
Moreover, although many fingerprint matching methods
have been developed to cope with distortions, most of them
are minutiae-based. Thus, they cannot use more topological
information (such as ridge shape) covering the entire fin-
gerprint image and the limitation of information still exists.
In addition, these methods use complex data structures and
many parameters for fingerprint matching. Accordingly, it is
hard to understand and implement these methods accurately.
Considering the facts mentioned above, instead of developing
complex distortion models or elaborate minutiae alignment
algorithms,weproposeanewand simple matching scheme
by incorporating conventional minutiae features and additional
ridge features associated with corresponding minutiae sets. To
extract the ridge features, a ridge-based coordinate system is
also defined. The ridge features consist of four elements: ridge
count (rc), ridge length (rl), ridge curvature direction (rcd), and
ridge type (rt). These features are invariant to any geometric
transformations (rotation, translation) of the fingerprints and
concisely represent the relationships between the minutiae since
the maintenance of ridge structures is robust to distortions.
Moreover, since the correlation between the proposed ridge
features and conventional minutiae features is low, combining
these features leads to an improvement in the overall recogni-
tion performance with a small increment in template size. Our
ridge features require only 5 bytes (ridge count—1 byte; ridge
length—2 bytes; ridge curvature direction—1 byte; and ridge
type—1 byte) for each minutiae pair.
This paper is organized as follows. In Section II, we intro-
duce and analyze the proposed ridge features extracted from
the ridge-based coordinate system. In Section III, we introduce
afingerprint matching algorithm incorporating conventional
minutiae and the proposed ridge features. Experimental results
are shown in Section IV and Section V offers conclusions and
suggestions for further research.
II. FINGERPRINT PREPROCESSING AND RIDGE
FEATURE EXTRACTION
A. Fingerprint Preprocessing
Before extracting the proposed ridge features, we need to per-
form some preprocessing steps (see Fig. 2). These steps include
typical feature extraction procedures as well as additional pro-
cedures for quality estimation and circular variance estimation.
We first divide the image into 8 8 pixel blocks. Then, the mean
and variance values of each block are calculated to segment the
Fig. 2. Overall preprocessing steps.
fingerprint regions in the image. We then apply the method de-
scribed in [13] to estimate the ridge orientation and the ridge
frequency is calculated using the method presented in [14]. The
Gabor filter [15] is applied to enhance the image and obtain a
skeletonized ridge image. Then, the minutiae (end points and bi-
furcations) are detected in the skeletonized image. The quality
estimation procedure [16] is performed in order to avoid ex-
tracting false minutiae from poor quality regions and to enhance
the confidence level of the extracted minutiae set. Furthermore,
in regions where ridge flows change rapidly, such as the area
around a singular point, it is hard to estimate the ridge orienta-
tions accurately or to extract the thinned ridge patterns consis-
tently. Therefore, to detect regions which have large curvature,
we apply circular variance estimation [17]. The circular vari-
ance of the ridge flows in a given block is calculated as follows:
(1)
where and represent the estimated orientation of the th
block and the number of neighboring blocks around the th
block, respectively. In our experiments, we use eight neigh-
boring blocks. Quality estimation and circular variance values
are used to avoid generating feature vectors in poor quality re-
gions or in regions around singular points. Moreover, we adopt
some postprocessing steps [18] to remove falsely extracted
ridges, such as short ridges and bridges. We can then extract
the ridge structures consistently against various noise sources.
B. Ridge Feature Extraction
1) Proposed Ridge-Based Coordinate System: After per-
forming the preprocessing steps, we obtain the skeletonized
ridges and minutiae information from the fingerprint image.
We can then define ridge coordinates and extract ridge features
between two minutiae. As shown in Fig. 3, each ridge-based
coordinate system is defined by a minutia (called origin) and
vertical and horizontal axes starting from the origin minutia.
First, the vertical axis is defined by drawing a line passing
through the origin and orthogonal to the orientation of the
origin. The axis also traverses the ridge flows orthogonally.
In addition, to define the sign of the vertical axis according
to the origin, the cross product between the orientation of the
340 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 6, NO. 2, JUNE 2011
Fig. 3. Ridge-based coordinate system.
origin and the vector pointing from the origin to the side of the
vertical axis is calculated as follows:
(2)
where ,,and represent the sign of the vertical axis, the
minutia orientation vector, and the unit vector of the vertical
axis, respectively. Thus, we determine the positive and the neg-
ative side of the vertical axis by checking the sign value of .
To represent the relative position of the minutiae (minutiae
,,and in Fig. 3) according to the origin, horizontal axes
should be defined. The horizontal axes are definedasridgesin-
tersecting the vertical axis. To define the sign of each horizontal
axis, the cross product between the vectors pointing from the
intersection to the vertical and horizontal axes is calculated as
follows:
(3)
where ,,and represent the sign of the horizontal axis,
the vector pointing from the intersection to the horizontal and
the vertical axis, respectively. In the ridge-based coordinate
system, the ridge features that describe the relationship be-
tween the origin (minutia in Fig. 3) and an arbitrary minutia
(minutiae ,,and in Fig. 3), are described as follows:
(4)
where rc, rl, rcd, and rt represent the ridge count, ridge length,
ridge curvature direction, and ridge type, respectively. These
four components form a ridge-based feature vector between two
minutiae and this feature vector is used in the matching process.
In the following sections, we will explain in detail these ridge
features were selected and the methods for extracting these
features.
2) Ridge Feature Extraction: In the general ridge count
methods [10], [11], the number of ridges that intersect the
straight line between two minutiae in the spatial domain is
counted. However, when the ridge-counting line is parallel
to the ridge structures, the line may meet the same ridge at
Fig. 4. Example of ridge-counting errors using the general ridge counting
methods. Even though the two images are from the same fingerprints, the ridge
count numbers between the two corresponding minutiae are different due to
skin deformation.
Fig. 5. Comparison of the ridge counting methods.
one point, at more than two points, or at no point, due to skin
deformation (see Fig. 4).
Therefore, unlike existing ridge-counting methods, here, the
ridge count (rc) is calculated by counting the number of ridges
along the vertical axis until the axis meets the ridge attached
to the neighboring minutia. The vertical axis is perpendicular
to the ridge structures. Thus, the counted numbers are less af-
fected by skin deformation than in the results of the general
ridge counting methods. In order to prove the effectiveness of
the proposed ridge counting method, we used 50 fingers from
FVC 2002 DB1-A [22] and manually paired the corresponding
minutiae among the five images from each finger. After pairing
two corresponding minutiae, we estimated the probability dis-
tributions of the absolute difference of the ridge counting num-
bers in each method. Fig. 5 shows that the absolute difference
of the ridge counting numbers using our method is smaller than
that using the conventional ridge count method. Therefore, we
can conclude that our ridge count feature is more robust to skin
deformation. Furthermore, to increase the discriminating power
of the ridge count (rc) feature, we also consider the direction of
the ridge count line. The ridge count (rc) is not always a posi-
tive number and the sign of the ridge count follows the sign of
the vertical axis. If two minutiae are directly connected by the
same ridge, the ridge count would be zero.
The ridge length (rl) is the distance on the horizontal axis
from the intersection of the vertical and horizontal axis to a
minutia. To prove the usefulness of the ridge length feature,
we conducted an experiment similar to the analysis of the ridge
CHOI et al.: FINGERPRINT MATCHING INCORPORATING RIDGE FEATURES WITH MINUTIAE 341
Fig. 6. Probability distribution of the absolute difference of ridge length.
Fig. 7. Ridge curvature direction. (a) Concave shape. (b) Convex shape.
count feature. Fig. 6 shows the probability distribution of the
absolute difference of the ridge length feature. As shown in
the figure, the absolute differences of ridge length elements are
mostly less than 16 pixels. Therefore, we can set the threshold
of the ridge length feature to determine the same fingerprint as
16 pixels. The ridge length value also has a sign and follows the
sign of the related horizontal axis to improve the discriminating
power.
To use more topology information in ridge patterns for
matching, the ridge curvature direction is also considered. As
shown in Fig. 7, even though the ridge count and ridge length
values are very similar, the shapes of the ridge patterns may be
different [concave shape—Fig. 7(a); convex shape—Fig. 7(b)].
The ridge curvature direction is defined as follows:
(5)
where represents the th vector between the sampling points
along the horizontal axis from the intersection of the vertical and
horizontal axes to the minutia (see Fig. 7) and represents
the number of sampling points. In our experiment, we set the
sampling point every 8 pixels on the ridges. Then, by checking
the sign of this value, we can determine the ridge curvature di-
rection. The ridge curvature direction feature is robust to skin
deformation but some errors may still occur. First, ridges may
have more than two inflection points, which makes it hard to de-
fine this feature. Second, some ridges are too straight to define a
curved direction. Therefore, to avoid the error caused by more
than two inflection points, we empirically limit the maximum
length of ridges to 80 pixels
Fig. 8. Examples of ridge types.
.Additionally,toavoidtheerrorcausedbyastraight
ridge, we defined the ridge curvature direction as 0.
Due to the feature extraction error, skin condition changes,
and different finger pressures, end points may appear as bifur-
cations and vice versa. Therefore, considering these facts and to
further improve the discriminating power of ridge features, the
ridge type (rt) is used as one of the ridge features instead of a
minutia type. To determine the ridge type (rt), each minutia is
first classified as an end point or a bifurcation. If a minutia is an
end point, there is only one ridge belonging to the minutia. If a
minutia is a bifurcation, there are three ridges connected to the
minutiae. Next, the type of ridge associated with the minutia is
determined as one of four types according to the type of the
minutia and the relative position of the ridges. As shown in
Fig. 8, if a minutia is an end point, the ridge type is defined as
. In a bifurcation case, the three ridges are labeled by checking
the angle between each ridge and the minutia orientation. A tri-
angle is created by three points on the ridges (equidistant from
the bifurcation). If the vertex of the triangle is not on the shortest
side of the triangle, then the ridge belongs to the vertex and is
defined as type . The other two ridges are classified as type
and , moving in a clockwise direction from .Gen-
erally speaking, ridge type can change only into ridge type
or . However, type cannot be converted into type .
Therefore, we use this information in the fingerprint matching.
The overall procedure for extracting ridge features is as
follows:
1) Perform preprocessing steps and extract a ridge image
from a fingerprint.
2) Traverse the ridge-valley structures along the vertical axis
from each minutia origin.
a) If the vertical axis intersects with the ridges attached
to a minutia, extract ridge features (ridge count, ridge
length, ridge curvature direction, and ridge type) from
the origin to the minutia and form a ridge feature
vector between the origin and the minutiae.
b) Keep traversing all the ridges until one of three termi-
nating conditions is satisfied (see below).
3) If all minutiae are used as the origin minutiae, terminate
the procedure. Otherwise, return to step 2).
The termination conditions include the following three cases:
1) The vertical axis reaches a background region in the fin-
gerprint image.
2) The vertical axis reaches a poor quality region in the fin-
gerprint image.
3) The vertical axis reaches a high circular variance region in
the fingerprint image.
342 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 6, NO. 2, JUNE 2011
III. FINGERPRINT MATC H I N G
The ridge feature vectors between the minutiae in the ridge
coordinate system can be expressed as a directional graph whose
nodes are minutiae and whose edges are ridge feature vectors.
Thus, we can adopt graph matching methods to utilize the ridge
feature vectors in fingerprint matching. Chikkerur et al. [19]
proposed a graph-based fingerprint minutiae matching method
in a Euclidean space. They first defined the local neighborhood
of each minutia, called -plet, which consists of the -nearest
minutiae from a center minutia. The comparison of two -plets
is performed by computing the distance between the two strings
obtained by concatenating the neighboring minutiae, sorted
by their radial distance with respect to the center minutia. Neigh-
borhoods are matched by dynamic programming and a match of
local neighborhoods is propagated with a breadth-first fashion.
Thus, we apply this matching scheme to our ridge-based coor-
dinate system, since the ridge-based coordinate system can be
represented as a graph and each coordinate system makes a local
neighborhood. Moreover, the data structure of the ridge-based
coordinate system is very similar to the -plet structure pro-
posed in [19].
The overall flow of the proposed fingerprint matching algo-
rithm is as follows:
1) Initially match any pair of ridge-based coordinate systems
extracted from the enrolled fingerprint image and the input
fingerprint image using dynamic programming.
2) Select the top degree of matched ridge-based coordinate
pairs.
3) For every initially matched pair, a breadth-first search
(BFS) is performed to detect the matched ridge-based
coordinate pairs incrementally.
4) Check the validity of the matched coordinate pairs using
the relative position and orientation of the minutiae and
count the number of matched minutiae.
5) Iterate steps 3) and 4) times and then return the max-
imum number of matched minutiae.
6) Compute the matching score.
Dynamic programming is applied to find the optimal solu-
tion in matching two string sequences in the enrolled and
input ridge-based coordinates. The ridge feature vectors in a
ridge-based coordinate system are arranged in the order of their
ridge count feature component (rc), then the order is invariant
intrinsically. Therefore, the feature vectors in a ridge-based
coordinate system can be stored as the elements of an ordered
sequence. Thus, all the enrolled and input ridge-based co-
ordinates are compared one by one and a similarity score is
computed for the dynamic programming. The similarity score
isbasedontheBayesian decision rule [20] and is calculated as
follows:
otherwise (6)
where is the absolute difference between two feature vec-
tors, is the correctly matched class, and is the incorrectly
matched class. In order to calculate the posterior probability, we
assumed that the prior probabilities of and are equal. We
estimated the conditional probability density functions
and by using a Parzen window of a uniform kernel in
the training set of FVC 2002 DB1. For the ridge feature vector,
Fig. 9. Examples of corresponding ridge feature vectors according to the
number of connection steps (upper and lower row images are from the same
finger).
Fig. 10. Example of matched minutiae using the proposed ridge feature vectors
(solid circles represent matched minutiae and dotted lines represent the vertical
axis of each minutia).
TAB L E I
EER COMPARISONS OF TWO MAT C H I N G METHODS ON FVC DATABASES
the three feature elements (ridge count, ridge length, and ridge
curvature direction) are used to calculate the scores and the ridge
type feature is used to check the validity of the candidate pairs.
After that, we select the top degree of matched ridge-based
coordinate pairs. In this paper, we set the value as 10. For
every initially matched pair, we perform a BFS to increment
the match for other neighboring ridge-coordinate systems. How-
ever, there is not always a path for every minutiae pair because
we do not extract ridge features in the fingerprint regions which
have low quality or a high curvature. Therefore, we find a de-
tour path to perform the BFS [21]. For example, even if it is
not possible to directly extract the ridge feature vector between
minutia and due to the absence of a path, it is still possible
to obtain the ridge feature vector by including minutia (as
). Fig. 9 shows some examples of the corresponding
ridge feature vectors using the detour, as the number of connec-
tion steps increases.
We check the validity of the matched coordinate pairs using
the relative position and orientation of the minutiae used in
conventional minutiae-based matching. If the relative position
CHOI et al.: FINGERPRINT MATCHING INCORPORATING RIDGE FEATURES WITH MINUTIAE 343
Fig. 11. ROC curves on each database.
and orientation of the minutiae in the coordinate pair are also
matched, we can be sure that these minutiae are correctly
matched. We then count the number of matched minutiae and
store them. Finally, after the execution of the BFS procedure
for every initial matched pair, we find the maximum number of
matched minutiae between two fingerprints. Fig. 10 shows an
example of matched minutiae using the proposed method. As
shown in the figure, even if two impressions of the same finger
are different due to skin distortion, many minutiae are matched
correctly.
To compute the matching score, we must consider both the
degree of overlap between two impressions and the degree of
similarity of the overlapped region. Thus, the matching score
can be computed as follows:
(7)
where ,,and are the number of matched minutiae, the
number of minutiae in an input image, and the number of minu-
tiae in a template image, respectively. and are the number
of minutiae in the overlapping regions of the query and tem-
plate images, respectively. The overlapped regions are where
two fingerprints intersect after the linear transformation (trans-
lation and rotation) using the matched minutiae.
IV. EXPERIMENTAL RESULTS AND ANALYSIS
We compared the recognition performances of two algo-
rithms (the conventional minutiae-based matching method [9]
and the proposed method). To demonstrate the effect of the pro-
posed ridge features more generally, we chose the conventional
minutiae-based method, which is based on popular minutiae
features such as minutiae position, minutiae orientation, and
minutiae type [9] instead of the state-of-the-art minutiae-based
algorithms which use additional specific matching techniques.
The conventional method utilizes several reference points
for local alignment and an adaptive tolerance box is used to
calculate the number of matched minutiae.
For the experiments, we used the databases FVC 2002 DB1,
DB2, DB3, and FVC 2004 DB1, released on the Web [22], [23].
Regarding fingerprint quality, FVC 2002 DB3 and FVC 2004
DB1 have lower quality fingerprints than other databases be-
cause the users were explicitly requested to exaggerate distor-
tions [1]. Therefore, it is reasonable to analyze the robustness of
the proposed method against skin distortions by using these
344 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 6, NO. 2, JUNE 2011
Fig. 12. Example of ridge length error caused by the ridge orientation estima-
tion error.
Fig. 13. Examples of fingerprint images with few ridge features.
databases. Each database is composed of 800 fingerprint images
from 100 different fingers (eight impressions per finger). For
genuine matches, each impression of each finger is compared
with other impressions of the same finger. Therefore, 2800
genuine matches were executed in each database. For imposter
matches, each impression of each finger is compared with
all impressions of the different fingers. Therefore, 316 800
imposter matches were conducted in each database. Table I
shows the equal error rate (EER) comparisons of two matching
methods on the FVC databases and Fig. 11 shows the ROC
curves on each database. From the experimental results, we can
see that the proposed method is superior to the conventional
minutiae-based one for all the databases. Even though the
performances for FVC 2002 DB3 and FVC 2004 DB1 are
lower than those for FVC 2002 DB1 and DB2, we can maintain
that our ridge features can support the minutiae features when
they are used together in the matching stage. Some recognition
errors occurred and can be analyzed in the following way.
The first cause is a wrongly estimated orientation error. As
shown in Fig. 12(b), the vertical axis starting from a minutia
(red-circle) is slightly tilted towards the vertical axis determined
from the corresponding minutia in Fig. 12(a) since the orienta-
tion fields in Fig. 12(c) are poorly estimated. As a result, there
is a large variation between the ridge length (rl) features in the
corresponding sets. Therefore, by enhancing the orientation es-
timation process, the performance can be improved. Second,
there are some minutiae pairs offering no ridge feature vec-
tors because some images had small foreground regions or their
levels of quality were too low, as can be seen in Fig. 13. In
Fig. 13(a), the foreground region was very small and there were
few minutiae. Moreover, only a few minutiae were located in the
region (high circular variance region) around a singular point.
In Fig. 13(b), the foreground region was split in two by a poor
quality region, so there was no connection between the minutiae
in the upper good quality region and those in the lower good
quality region. This disturbed the generation of ridge feature
vectors in the whole fingerprint region, reducing the discrim-
inating power. And the experiments were conducted on a PC
with Core2 Duo 2.4 GHz. The average matching time of the
proposed method was 83 ms.
V. C ONCLUSIONS AND SUGGESTIONS FOR FUTURE WORK
In this paper, we proposed a novel fingerprint matching algo-
rithm using both ridge features and the minutiae. The ridge fea-
tures consist of four elements (ridge count, ridge length, ridge
curvature direction, and ridge type) that describe the relation-
ship between the minutiae. With the proposed ridge features and
conventional minutiae features (minutiae type, orientation, and
position), we proposed a novel matching scheme using a BFS
to detect the matched minutiae pairs. The experimental results
show that the proposed method gives higher matching scores
compared to the conventional minutiae-based one. Hence we
can conclude that the proposed ridge features give additional
information for fingerprint matching with little increment of
template size. And, for future work, we will try to incorporate
these features into the state-of-the-art minutiae-based matchers
for further improvement of the matching performance. Also, our
matching method needs to be improved for images with a small
foreground area and those of low quality. Therefore, in future
work, we will develop the use of global knowledge of finger-
prints, such as singular point position, to enhance the matching
accuracy. We will also develop a robust preprocessing method
to reduce enhancement errors. Moreover, our ridge features can
be used in other applications. In the area of fingerprint identifi-
cation, it is important to be able to extract alignment-free fea-
tures since it needs no time to align a query feature set with
the enrolled feature sets one by one [24]. In cancellable fin-
gerprints, without a fiducial correspondingpairsuchasacore
point, it is difficult to align a transformed feature set with an
enrolled one [25]. The proposed ridge features are invariant to
any transform, thus they can be used in addition to conventional
alignment-free features in the fingerprint identification or can-
cellable fingerprint area.
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Heeseung Choi received the B.S. and the M.S.
degrees in electrical and electronic engineering from
Yonsei University, Seoul, Korea, in 2004 and 2006,
respectively. He is currently working toward the
Ph.D. degree in electrical and electronic engineering
from Yonsei University, Seoul, Korea.
He has been a research member of the Biometrics
Engineering Research Center (BERC). His research
interests include computer vision, biometrics, image
processing, and pattern recognition.
Kyoungtaek Choi received the B.S. degree in elec-
trical and electronics engineering from Chung-ang
University, Seoul, Korea, in 2001, and the M.S.
and Ph.D. degrees in electrical and electronic en-
gineering from Yonsei University, Seoul, Korea, in
2003 and 2008, respectively.
Currently, he is a research engineer in LIG Nex1,
Yong In, Korea. His research interests include com-
puter vision, biometrics, image processing, and pat-
tern recognition.
Jaihie Kim received the B.S. degree in electronic
engineering from Yonsei University, Seoul, Korea,
in 1979, and the M.S. degree in data structures and
the Ph.D. degree in artificial intelligence from Case
Western Reserve University, Cleveland, OH, in 1982
and 1984, respectively.
Since1984,hehasbeenaprofessorintheSchool
of Electrical and Electronic Engineering, Yonsei Uni-
versity. He is currently the Director of the Biometric
Engineering Research Center in Korea. His research
areas include biometrics, computer vision, and pat-
tern recognition.
Prof. Kim is currently the Chairman of Korean Biometric Association.
